tetano
Editor, Senior Moderator
Med Decis Making. 2013 Mar 20. [Epub ahead of print]
Continuously Rethinking the Definition of Influenza for Surveillance Systems: A Dependent Bayesian Expert System.
Alemi F, Atherton MJ, Pattie DC, Torii M.
Source
District of Columbia Veteran Administration Medical Center, Washington, DC (FA).
Abstract
OBJECTIVE:
. In the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), influenza was originally defined by a list of 29 and later by a list of 12 diagnosis codes. This article describes a dependent Bayesian procedure designed to improve the ESSENCE system and exploit multiple sources of information without being biased by redundancy.
METHODS:
We obtained 13,096 cases within the Armed Forces Health Longitudinal Technological Application electronic medical records that included an influenza laboratory test. A Dependent Bayesian Expert System (D-BESt) was used to predict influenza from diagnoses, symptoms, reason for visit, temperature, month of visit, category of enrollment, and demographics. For each case, D-BESt sequentially selects the most discriminating piece of information, calculates its likelihood ratio conditioned on previously selected information, and updates the case's probability of influenza.
RESULTS:
When the analysis was limited to definitions based on diagnoses and was applied to a sample of patients for whom laboratory tests had been ordered, the areas under the receiver operating characteristic curve (AUCs) for the previous (29-diagnosis) and current (12-diagnosis) ESSENCE lists and the D-BESt algorithm were, respectively, 0.47, 0.36, and 0.77. Including other sources of information further improved the AUC for D-BESt to 0.79. At the best cutoff point for D-BESt, where the receiver operating characteristic curve for D-BESt is farthest from the diagonal line, the D-BESt algorithm correctly classified 84% of cases (specificity = 88%, sensitivity = 62%). In comparison, the current ESSENCE approach of using a list of 12 diagnoses correctly classified only 31% of this sample of cases (specificity = 29%, sensitivity = 42%).
CONCLUSIONS:
False alarms in ESSENCE surveillance systems can be reduced if a probabilistic dynamic learning system is used.
PMID:
23515214
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/23515214
Continuously Rethinking the Definition of Influenza for Surveillance Systems: A Dependent Bayesian Expert System.
Alemi F, Atherton MJ, Pattie DC, Torii M.
Source
District of Columbia Veteran Administration Medical Center, Washington, DC (FA).
Abstract
OBJECTIVE:
. In the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), influenza was originally defined by a list of 29 and later by a list of 12 diagnosis codes. This article describes a dependent Bayesian procedure designed to improve the ESSENCE system and exploit multiple sources of information without being biased by redundancy.
METHODS:
We obtained 13,096 cases within the Armed Forces Health Longitudinal Technological Application electronic medical records that included an influenza laboratory test. A Dependent Bayesian Expert System (D-BESt) was used to predict influenza from diagnoses, symptoms, reason for visit, temperature, month of visit, category of enrollment, and demographics. For each case, D-BESt sequentially selects the most discriminating piece of information, calculates its likelihood ratio conditioned on previously selected information, and updates the case's probability of influenza.
RESULTS:
When the analysis was limited to definitions based on diagnoses and was applied to a sample of patients for whom laboratory tests had been ordered, the areas under the receiver operating characteristic curve (AUCs) for the previous (29-diagnosis) and current (12-diagnosis) ESSENCE lists and the D-BESt algorithm were, respectively, 0.47, 0.36, and 0.77. Including other sources of information further improved the AUC for D-BESt to 0.79. At the best cutoff point for D-BESt, where the receiver operating characteristic curve for D-BESt is farthest from the diagonal line, the D-BESt algorithm correctly classified 84% of cases (specificity = 88%, sensitivity = 62%). In comparison, the current ESSENCE approach of using a list of 12 diagnoses correctly classified only 31% of this sample of cases (specificity = 29%, sensitivity = 42%).
CONCLUSIONS:
False alarms in ESSENCE surveillance systems can be reduced if a probabilistic dynamic learning system is used.
PMID:
23515214
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/23515214